基于特征的图像检索技术研究
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摘要
伴随着网络和多媒体获取技术的快速发展,数字图像的数量急剧增长。如何从海量数字图像集合中为用户快速检索目标图像,已成为信息领域亟待解决的关键问题。在此背景下,图像检索技术近年来迅猛发展,引起了学术界和产业界的高度关注。现有的搜索引擎普遍采用传统的基于文本标注的图像搜索。这种方法具有人工标注耗时费力、主观多义的局限,已经难以适用海量网络数据库的检索需求。为此,研究人员提出了基于内容的图像检索来克服这一局限。基于内容的图像检索直接从图像本身出发考虑,尽可能地从与人类视觉相似的角度去描述图片的固有属性,查找与查询图片相似的图像集合提供给用户。在这一过程中,图像的特征表示是核心问题,直接影响基于内容的检索系统的性能。
本文从全局和局部特征两方面研究分析了常用的图像特征表示,并在以下几方面开展了创新性工作。本文的主要工作包括以下几点：
1.基于颜色特征和纹理特征的图像检索算法
单一特征的检索由于特征本身对图像描述的局限性,在复杂图像库上完成检索时,不能得到较好的检索效果。本文综合颜色特征和LBP纹理特征的实现了图像检索。实验结果表明,基于单一特征的检索算法,在完成特征内部归一化和特征间归一化后,其结果优于未进行特征归一化的算法,表明归一化的必要性；在结合多特征后,其检索效率明显上升。
2.提出了一种基于双树复数小波的显著点检测算法,并结合图像的局部和全局特征实现了图像检索
本文提出的基于双树复数小波变换的显著点检测算法是在图像变换后的多尺度空间上完成的。利用同尺度层内多个方向上的复小波分解系数得到能量图,提取能量图中的局部极值点作为图像显著点。在此基础上,对显著点所在区域进行环形分割,统计每个环形区域内的显著点邻域的颜色直方图；计算显著点的离散度作为其几何特征；并结合图像复小波变换多尺度层上的幅值和相位特征完成图像描述,实现图像检索。实验部分先分析了Harris角点检测、基于高斯差分的显著点检测与本文提出的检测算法的时间复杂度；继而在Oxford数据集上利用重复率对三种检测算法的性能进行评价；并在Corel库上实现图像检索。
3.不同局部纹理特征的理论比较和在图像检索上的比较
本文从理论方面分析了CENTRIST和LBP两种相似的纹理特征表示的同异。同时,结合具有空间分布信息的多尺度金字塔分割,实现了基于CENTRIST的图像检索,并在两个数据库上完成了实验。实验结果先是表明直方图交叉核比欧式距离更适用于CENTRIST特征表示。为了确定两者差异的重要性,本文提出了与CENTRIST更接近的逆LBP的特征表示。实验验证三种纹理特征中CENTRIST的性能最优,也揭示了CENTRIST与LBP的最大区别在于其在图像相邻像素之间具有约束性和传导性。
4.提出了一种对特征包表示进行动态加权的算法
图像的特征包表示利用视觉词的无序组合表示一幅图像,属于中层语义特征,已被成功地应用于大尺度图像库的检索和分类。对特征包的改进涉及特征描述,视觉词典的建立,特征量化和后处理等多个方面。本文通过分析相似图像之间特征包表示,提出了一种基于相关反馈的动态加权的算法,并在两个数据集上定量地分析了改进算法,同时与基本的基于特征包的图像检索算法相比较。实验结果表明本算法有效提升了前N个返回图像的准确率。
With the development of Internet and the acquisition technology of multimedia, the amount of digital images is increasing tremendously. How to retrieve the target images quickly and correctly from large scale image database for users is the key problem to be solved exigently in the domain of information. Under the circumstance, image retrieval has been developed and studied widely. Text-based image retrieval is popular in the web search engine, which is based on textual labels by human annotating. The disadvantages are time-consuming, subjectivity and ambiguity. And it is hard to meet the requirements of retrieval on large web database. To handle this problem, content-based image retrieval is proposed which aims to find the similar images with the query based on the inherent property of images. This techonolgy analyzes the images directly justlike the human visual system. Feature representation of image is the key process, and it plays a direct role on retrieval.
This paper studied feature representations of image from two aspects of local and global. The main work including:
1. Image retrieval based on color and texture features.
Single feature-based image retrieval can not achieve higher precision and recall on complex image database because of its limitation. This paper exploits color histogram and LBP texture feature for image retrieval. The experiments show that internal and external normalizations' of features are efficient for retrieval based on single feature, and multi-features can bring higher precision and recall than single feature.
2. A novel keypoint detector based on the dual tree complex wavelet transform is proposed. Based on this detector, local and global fetures are combined for image retrieval.
This algorithm is performed in complex wavelet pyramid space of an image. It uses the intra-scale coefficients'product to obtain the energy map. And then extracts the local extrema of the map as the salient keypoints. Based on this detector, the image is divided into several concentric circles according to the distribution of salient points. And then, the annular color histogram is exploited to describe the local color information, the divergence is computed as the geometry feature of an image, and magnitude and phase features at different scales of the complex wavelet transform decomposition of image are extracted for image retrieval. The comparison of complexity among Harris detector, DoG-based detector and the detector proposed in this paper is analyzed. And the experiments on Oxford affine database are performed with the evaluation of repeatability. The results of image retrieval show the effectiveness of our proposed algorithm.
3. Theory and empirical Comparisons between two local textural features on CBIR.
In this paper, the differences between CENTRIST and LBP are analyzed on theory. And they are integrated with the spatial information by multi scale spatial pyramid for content-based image retrieval. The experimental results firstly show that the similarity of two images computed by histogram intersection can achieve better result than computed by Euclidean distance for CENTRIST descriptor. For analyzing the impact of the differences, reverse LBP is performed, which is more similar with CENTRIST. The results demonstrate the most important of differences between CENTRIST and LBP is that whether the constraints and the transitivity among neighbored pixels exist.
4. A dynamically weighting scheme for Bag-of-features based image retrieval.
Bag-of-Features (BoF) representation of image, which is a middle-level semantic feature, is consisted of an orderless collection of visual words, and successfully used in retrieval and classification of large scale image repository. Several extensions have been proposed that involve feature description, dictionary building, feature encoding and post-query process, etc. This paper proposes a dynamically weighting scheme for BoF-based image retrieval based on feedback. We quantitatively evaluate the proposed method on two different databases. Experiments confirm that the proposed weighting scheme has better performance than the baseline of BoF-based image retrieval systems. Meanwhile, the results demonstrate the effectiveness of the weighting scheme in terms of the precision of top-N returned images.
本文从全局和局部特征两方面研究分析了常用的图像特征表示,并在以下几方面开展了创新性工作。本文的主要工作包括以下几点：
1.基于颜色特征和纹理特征的图像检索算法
单一特征的检索由于特征本身对图像描述的局限性,在复杂图像库上完成检索时,不能得到较好的检索效果。本文综合颜色特征和LBP纹理特征的实现了图像检索。实验结果表明,基于单一特征的检索算法,在完成特征内部归一化和特征间归一化后,其结果优于未进行特征归一化的算法,表明归一化的必要性；在结合多特征后,其检索效率明显上升。
2.提出了一种基于双树复数小波的显著点检测算法,并结合图像的局部和全局特征实现了图像检索
本文提出的基于双树复数小波变换的显著点检测算法是在图像变换后的多尺度空间上完成的。利用同尺度层内多个方向上的复小波分解系数得到能量图,提取能量图中的局部极值点作为图像显著点。在此基础上,对显著点所在区域进行环形分割,统计每个环形区域内的显著点邻域的颜色直方图；计算显著点的离散度作为其几何特征；并结合图像复小波变换多尺度层上的幅值和相位特征完成图像描述,实现图像检索。实验部分先分析了Harris角点检测、基于高斯差分的显著点检测与本文提出的检测算法的时间复杂度；继而在Oxford数据集上利用重复率对三种检测算法的性能进行评价；并在Corel库上实现图像检索。
3.不同局部纹理特征的理论比较和在图像检索上的比较
本文从理论方面分析了CENTRIST和LBP两种相似的纹理特征表示的同异。同时,结合具有空间分布信息的多尺度金字塔分割,实现了基于CENTRIST的图像检索,并在两个数据库上完成了实验。实验结果先是表明直方图交叉核比欧式距离更适用于CENTRIST特征表示。为了确定两者差异的重要性,本文提出了与CENTRIST更接近的逆LBP的特征表示。实验验证三种纹理特征中CENTRIST的性能最优,也揭示了CENTRIST与LBP的最大区别在于其在图像相邻像素之间具有约束性和传导性。
4.提出了一种对特征包表示进行动态加权的算法
图像的特征包表示利用视觉词的无序组合表示一幅图像,属于中层语义特征,已被成功地应用于大尺度图像库的检索和分类。对特征包的改进涉及特征描述,视觉词典的建立,特征量化和后处理等多个方面。本文通过分析相似图像之间特征包表示,提出了一种基于相关反馈的动态加权的算法,并在两个数据集上定量地分析了改进算法,同时与基本的基于特征包的图像检索算法相比较。实验结果表明本算法有效提升了前N个返回图像的准确率。
With the development of Internet and the acquisition technology of multimedia, the amount of digital images is increasing tremendously. How to retrieve the target images quickly and correctly from large scale image database for users is the key problem to be solved exigently in the domain of information. Under the circumstance, image retrieval has been developed and studied widely. Text-based image retrieval is popular in the web search engine, which is based on textual labels by human annotating. The disadvantages are time-consuming, subjectivity and ambiguity. And it is hard to meet the requirements of retrieval on large web database. To handle this problem, content-based image retrieval is proposed which aims to find the similar images with the query based on the inherent property of images. This techonolgy analyzes the images directly justlike the human visual system. Feature representation of image is the key process, and it plays a direct role on retrieval.
This paper studied feature representations of image from two aspects of local and global. The main work including:
1. Image retrieval based on color and texture features.
Single feature-based image retrieval can not achieve higher precision and recall on complex image database because of its limitation. This paper exploits color histogram and LBP texture feature for image retrieval. The experiments show that internal and external normalizations' of features are efficient for retrieval based on single feature, and multi-features can bring higher precision and recall than single feature.
2. A novel keypoint detector based on the dual tree complex wavelet transform is proposed. Based on this detector, local and global fetures are combined for image retrieval.
This algorithm is performed in complex wavelet pyramid space of an image. It uses the intra-scale coefficients'product to obtain the energy map. And then extracts the local extrema of the map as the salient keypoints. Based on this detector, the image is divided into several concentric circles according to the distribution of salient points. And then, the annular color histogram is exploited to describe the local color information, the divergence is computed as the geometry feature of an image, and magnitude and phase features at different scales of the complex wavelet transform decomposition of image are extracted for image retrieval. The comparison of complexity among Harris detector, DoG-based detector and the detector proposed in this paper is analyzed. And the experiments on Oxford affine database are performed with the evaluation of repeatability. The results of image retrieval show the effectiveness of our proposed algorithm.
3. Theory and empirical Comparisons between two local textural features on CBIR.
In this paper, the differences between CENTRIST and LBP are analyzed on theory. And they are integrated with the spatial information by multi scale spatial pyramid for content-based image retrieval. The experimental results firstly show that the similarity of two images computed by histogram intersection can achieve better result than computed by Euclidean distance for CENTRIST descriptor. For analyzing the impact of the differences, reverse LBP is performed, which is more similar with CENTRIST. The results demonstrate the most important of differences between CENTRIST and LBP is that whether the constraints and the transitivity among neighbored pixels exist.
4. A dynamically weighting scheme for Bag-of-features based image retrieval.
Bag-of-Features (BoF) representation of image, which is a middle-level semantic feature, is consisted of an orderless collection of visual words, and successfully used in retrieval and classification of large scale image repository. Several extensions have been proposed that involve feature description, dictionary building, feature encoding and post-query process, etc. This paper proposes a dynamically weighting scheme for BoF-based image retrieval based on feedback. We quantitatively evaluate the proposed method on two different databases. Experiments confirm that the proposed weighting scheme has better performance than the baseline of BoF-based image retrieval systems. Meanwhile, the results demonstrate the effectiveness of the weighting scheme in terms of the precision of top-N returned images.
引文
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